Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration

This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe

Advancing the use of passive sampling in risk assessment and management of contaminated sediments: Results of an international passive sampling inter-laboratory comparison

This work presents the results of an international interlaboratory comparison on ex situ passive sampling in sediments. The main objectives were to map the state of the science in passively sampling sediments, identify sources of variability, provide recommendations and practical guidance for standardized passive sampling, and advance the use of passive sampling in regulatory decision making by increasing confidence in the use of the technique. The study was performed by a consortium of 11 laboratories and included experiments with 14 passive sampling formats on 3 sediments for 25 target chemicals (PAHs and PCBs). The resulting overall interlaboratory variability was large (a factor of ∼10), but standardization of methods halved this variability. The remaining variability was primarily due to factors not related to passive sampling itself, i.e., sediment heterogeneity and analytical chemistry. Excluding the latter source of variability, by performing all analyses in one laboratory, showed that passive sampling results can have a high precision and a very low intermethod variability

Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources

The penetration of renewable energy sources (RESs) is increasing in modern power systems. However, the uncertainties of RESs pose challenges to distribution system operations, such as RES curtailment. Demand response (DR) and battery energy storage systems (BESSs) are flexible countermeasures for distribution-system operators. In this context, this study proposes an optimization model that considers DR and BESSs and develops a simulation analysis platform representing a medium-sized distribution system with high penetration of RESs. First, BESSs and DR were employed to minimize the total expenses of the distribution system operation, where the BESS model excluding binary state variables was adopted. Second, a simulation platform based on a modified IEEE 123 bus system was developed via MATLAB/Simulink for day-ahead scheduling analysis of the distribution system with a high penetration of RESs. The simulation results indicate the positive effects of DR implementation, BESS deployment, and permission for electricity sales to the upper utility on decreasing RES curtailment and distribution system operation costs. Noticeably, the RES curtailments became zero with the permission of bidirectional power flow. In addition, the adopted BESS model excluding binary variables was also validated. Finally, the effectiveness of the developed simulation analysis platform for day-ahead scheduling was demonstrated

Proactive and Sustainable Transport Investment Strategies to Balance the Variance of Land Use and House Prices: A Korean Case

The transport infrastructure sustaining the ascension of land values while synergizing with the industries is a condition optimized for economic sustainability. In general, although transport investment aims to create a more reliable, less congested, better-connected transport network, the secondary aim is to facilitate balanced and sustainable development by enhancing accessibility to infrastructures. Although the current investment principle in Korea more or less reflects the primary purpose, the second aim is not fully reflected and might be too strict about measuring the balanced and sustainable influence on the regional economy. Considering that the house price is an output of regional production, this research tried to establish more proactive quantitative models explaining how ‘transport accessibility to infrastructure’ raises the apartment price in South Korea while interacting with the industries. This study achieved four main results according to the model. First, most urban infrastructures raise apartment prices per square meter about ten times higher than most industries, given a percentage change. Second, the synergy between industrial sales and infrastructural accessibility was negative in most cases, showing a limit of infrastructural investment alone to facilitate sustainable development. Third, an impoverished area tends to conclude positive synergies between industries and infrastructures, justifying more infrastructural investment in those poor areas. Finally, a public service behaves as infrastructure, which re-examines public services’ functionality of the prime water. Conclusively, this research proved that accessibility to core infrastructures is essential in conjunction with land use status resulting from industrial geography to rebalance Korean apartment prices for sustainable investment in transportation sectors

Proactive and Sustainable Transport Investment Strategies to Balance the Variance of Land Use and House Prices: A Korean Case

The transport infrastructure sustaining the ascension of land values while synergizing with the industries is a condition optimized for economic sustainability. In general, although transport investment aims to create a more reliable, less congested, better-connected transport network, the secondary aim is to facilitate balanced and sustainable development by enhancing accessibility to infrastructures. Although the current investment principle in Korea more or less reflects the primary purpose, the second aim is not fully reflected and might be too strict about measuring the balanced and sustainable influence on the regional economy. Considering that the house price is an output of regional production, this research tried to establish more proactive quantitative models explaining how ‘transport accessibility to infrastructure’ raises the apartment price in South Korea while interacting with the industries. This study achieved four main results according to the model. First, most urban infrastructures raise apartment prices per square meter about ten times higher than most industries, given a percentage change. Second, the synergy between industrial sales and infrastructural accessibility was negative in most cases, showing a limit of infrastructural investment alone to facilitate sustainable development. Third, an impoverished area tends to conclude positive synergies between industries and infrastructures, justifying more infrastructural investment in those poor areas. Finally, a public service behaves as infrastructure, which re-examines public services’ functionality of the prime water. Conclusively, this research proved that accessibility to core infrastructures is essential in conjunction with land use status resulting from industrial geography to rebalance Korean apartment prices for sustainable investment in transportation sectors

Optimal Limited-stop Bus Routes Selection Using a Genetic Algorithm and Smart Card Data

In recent years, express bus service has come into the spotlight by overcoming slow bus operating speeds while maintaining its accessibility when it operates with local bus services. This study developed an optimal limited-stop bus routes selection (LSBRS) guideline as a scenario-based analysis and compared it with case study results. Smart card data and a genetic algorithm (GA) were used to develop the model with different scenarios. Then, total travel time savings as a result of implementing limited-stop bus service generated by the GA model were computed. The effectiveness of each factor was verified by multiple regression analysis, and the LSBRS methodology was determined. This methodology was applied to Suwon, Korea, as a case study. As a result, travel time savings were estimated to be 9.0–19.0%. The ranking of the total travel time savings proposed by the LSBRS methodology presented a similar tendency with that of the case-study analysis

Analysis of Influencing Factors in Purchasing Electric Vehicles Using a Structural Equation Model: Focused on Suwon City

The global automobile market is promoting the introduction of eco-friendly vehicles such as electric vehicles and hydrogen vehicles. However, disadvantages such as expensive prices and limited mileage compared to internal combustion engine vehicles have become obstacles to the expansion of eco-friendly vehicles. Therefore, in this study, a survey was conducted on the purchase of electric vehicles for citizens of Suwon. Using the survey data, a structural equation model was constructed to analyze the factors affecting the purchase of electric vehicles, which are eco-friendly vehicles. The results indicate that a lack of information and government policy on EV, the level of EV recognition and subsidy policy do not have an effect on EV purchase. However, charging infrastructure, battery performance and safety, operating conditions including ramps or use of heaters and air conditioners, subsidy effects and charging services demonstrate positive effects on EV purchase. Using direct and indirect effect analysis, the study shows that higher government subsidy and visiting charging services are the two most influential factors on EV purchase, followed by EV driving environment, charging infrastructure, battery performance and safety, and a lack of information and electric vehicle supply policy

Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting

Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud cover have a high correlation. However, because the predicted solar irradiance is not provided by the Meteorological Administration or a weather site, cloud cover can be used instead of the predicted solar radiation. A lot of information, such as the direction and speed of movement of the cloud is contained in the satellite image. Therefore, the spatio-temporal correlation of the cloud is obtained from satellite images, and this correlation is presented pictorially. When the learning is complete, the current satellite image can be entered at the current time and the cloud value for the desired time can be obtained. In the case of the predictive model, the artificial neural network (ANN) model with the identical hyperparameters or setting values is used for data performance evaluation. Four cases of forecasting models are tested: cloud cover, visible image, infrared image, and a combination of the three variables. According to the result, the multivariable case showed the best performance for all test periods. Among single variable models, cloud cover presented a fair performance for short-term forecasting, and visible image presented a good performance for ultra-short-term forecasting

Analysis of Influencing Factors in Purchasing Electric Vehicles Using a Structural Equation Model: Focused on Suwon City

The global automobile market is promoting the introduction of eco-friendly vehicles such as electric vehicles and hydrogen vehicles. However, disadvantages such as expensive prices and limited mileage compared to internal combustion engine vehicles have become obstacles to the expansion of eco-friendly vehicles. Therefore, in this study, a survey was conducted on the purchase of electric vehicles for citizens of Suwon. Using the survey data, a structural equation model was constructed to analyze the factors affecting the purchase of electric vehicles, which are eco-friendly vehicles. The results indicate that a lack of information and government policy on EV, the level of EV recognition and subsidy policy do not have an effect on EV purchase. However, charging infrastructure, battery performance and safety, operating conditions including ramps or use of heaters and air conditioners, subsidy effects and charging services demonstrate positive effects on EV purchase. Using direct and indirect effect analysis, the study shows that higher government subsidy and visiting charging services are the two most influential factors on EV purchase, followed by EV driving environment, charging infrastructure, battery performance and safety, and a lack of information and electric vehicle supply policy